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Record W2172801629

Procedural generation of a city quarter

2015· dissertation· en· W2172801629 on OpenAlexaboutno aff
Jan Juvan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsnot available
Fundersnot available
KeywordsProgrammerQuarter (Canadian coin)Computer scienceWork (physics)EngineeringGeographyProgramming language
DOInot available

Abstract

fetched live from OpenAlex

The goal of the thesis is to show the advantage of procedural content gener- ation and to review the current situation in this eld. Reducing development costs is an excellent tactic to obtain a competitive advantage over other companies that are engaged in developing games, which can be achieved by removing the need for a larger number of designers. We can achieve that with the introduction of procedural algorithms for generat- ing content. A programmer in cooperation with an artist can, in this way, achieve much more than a large group of artists. These algorithms also allow individual artists or small businesses to create content-rich games and allow for the creation of unexpected forms. To create a program that generates content, one needs a clear understand- ing of the content being generated, in the case of this work, city quarter. The thesis deals with the urban form and analysis of its components. The second objective of this thesis is to write a program for generating city quarters, the result of which achieves the desired characteristics for use in games. Created levels should be playable, visually appealing and not give the impression of being generated by a procedural algorithm, but rather seem to be the work of humans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.343
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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